Ollama
Run open-weight LLMs locally with a CLI, REST API, and OpenAI-compatible endpoints
Last reviewed 2026-06-20
Ollama is open-source (MIT-licensed) infrastructure for downloading, running, and serving open-weight large language models on your own machine. It packages models, weights, and configuration into a single artifact and exposes them through a command-line interface, a local REST API, and OpenAI-compatible endpoints, handling model downloads, GPU acceleration, and the local server automatically. It runs on macOS, Windows, and Linux, ships an official Docker image, and has first-party Python and JavaScript libraries. Ollama is plumbing for agents and LLM apps, not an agent itself. It serves models (Llama, Mistral, Qwen, Gemma, DeepSeek, gpt-oss, and others from its model library) and exposes model-level features such as tool calling, vision/multimodal input, embeddings, and structured outputs. Developers point frameworks, agent runtimes, and IDE assistants at the local Ollama endpoint to get private, offline inference without sending data to a hosted API. An optional Ollama Cloud tier runs larger models on remote hardware via the same API.
What it can do
Run open-weight LLMs locally
AssistantDownload and run models such as Llama, Mistral, Qwen, Gemma, DeepSeek, and gpt-oss from the Ollama library with one command; the tool handles weights, configuration, and GPU acceleration.
sourceServe models over a local REST and OpenAI-compatible API
AssistantExposes a local REST API plus OpenAI-compatible endpoints so existing client code and frameworks can call locally hosted models with minimal changes.
sourceExpose model-level tool calling, vision, and embeddings
AssistantPasses through model capabilities including tool/function calling, vision/multimodal input, embedding generation, and structured outputs; Ollama serves these, it does not act on them itself.
sourceOptional cloud inference for larger models
AssistantOllama Cloud runs larger open-weight models on remote hardware through the same API, with paid Pro and Max tiers for higher concurrency, per the official site.
source
Strengths
- +Easiest way to download, run, and serve open-weight models locally across macOS, Windows, and Linux
- +OpenAI-compatible API plus official Python and JavaScript libraries make it a drop-in local backend for agents and apps
- +Open source (MIT), private, and offline by default, with an optional cloud tier for larger models
Limitations
- −Infrastructure, not an agent: it serves models but does not plan, act, or orchestrate on its own
- −Performance and model quality are bounded by local hardware unless you use the paid cloud tier
- −Geared to developers; not an end-user product without a separate UI such as Open WebUI
Overview
Ollama is open-source (MIT) infrastructure for running open-weight large language models on your own hardware. It packages a model's weights and configuration into a single artifact and serves it through a CLI, a local REST API, and OpenAI-compatible endpoints, handling downloads, GPU acceleration, and the local server for you. It runs on macOS, Windows, and Linux, ships an official Docker image, and has first-party Python and JavaScript libraries.
What it does
With one command Ollama downloads and runs models from its library (Llama, Mistral, Qwen, Gemma, DeepSeek, gpt-oss, and more). It exposes model-level features including tool/function calling, vision/multimodal input, embeddings, and structured outputs. Ollama serves these capabilities; it does not act on them itself. It is the local backend that agent frameworks, IDE assistants, and LLM apps call.
Integrations & setup
Install the macOS, Windows, or Linux build (or run the official Docker image), then ollama run <model>. The OpenAI-compatible endpoint and the Python/JavaScript libraries let existing code target a local model with minimal changes. It is commonly paired with LangChain, LlamaIndex, and UIs such as Open WebUI.
Pricing
The runtime is free and open source under the MIT license. Ollama Cloud, which runs larger open-weight models on remote hardware through the same API, has a free tier plus paid Pro (from $20/mo) and Max ($100/mo) plans, per the official site.
Best for / not for
Best for developers and teams who want private, offline, or cost-controlled inference over open-weight models, and as the local model backend for agents and assistants. Not a turnkey agent or end-user chat product on its own, and bounded by local hardware unless you use the cloud tier.
Alternatives
For building agents and RAG on top of a model, LangChain and LlamaIndex are framework-level alternatives. As a local-runtime alternative, other local model servers exist; Ollama is differentiated by its ease of setup and OpenAI-compatible API.
What people are saying
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FAQ
Is Ollama free?+
Yes. The Ollama runtime is open source under the MIT license and free to run locally. Ollama Cloud, which runs larger models on remote hardware through the same API, is a separate offering with a free tier and paid Pro (from $20/mo) and Max ($100/mo) plans, per the official site.
Is Ollama an AI agent?+
No. Ollama is infrastructure for running and serving open-weight LLMs locally. It exposes model capabilities such as tool calling and vision through its API, but it does not plan or act autonomously. Developers point an agent framework or assistant at the local Ollama endpoint to build an agent on top of it.
What models does Ollama run?+
Open-weight models from its library, including Llama, Mistral, Qwen, Gemma, DeepSeek, and gpt-oss, among others. It is model-agnostic within the set of supported open-weight models.
Sources
- Ollama (official site) · accessed 2026-06-20
- ollama/ollama on GitHub · accessed 2026-06-20
- Ollama documentation · accessed 2026-06-20
Last reviewed 2026-06-20